x_values=np.linspace(0,1,100)marginal_density_values=[marginal_pdf_x(x)forxinx_values]marginal_distribution_values=[marginal_cdf_x(x)forxinx_values]plt.figure(figsize=(12,6))# 边缘密度函数图plt.subplot(1,2,1)plt.plot(x_values,marginal_density_values,label='边缘密度函数 f_X(x)')plt....
累积分布函数显示了数据小于或等于某个值的概率。我们可以使用Matplotlib来绘制CDF。 # 计算CDF data_sorted = np.sort(data) cdf = np.arange(1, len(data_sorted) + 1) / len(data_sorted) 绘制CDF plt.plot(data_sorted, cdf) 添加标题和标签 plt.title('Cumulative Distribution Function') plt.xlabel(...
orientation:可选,指定直方图的方向,包括垂直(v)和水平(h)。 cumulative:可选,指定是否绘制累积分布函数(cumulative distribution function,CDF)。 histfunc:可选,指定直方图的统计方式,包括计数(count)、求和(sum)、平均数(avg)、中位数(median)、最大值(max)和最小值(min)。 barmode:可选,指定多组数据的绘制...
density=True,bins=50)x=np.linspace(0.0001,8.0,1000)pdf=1/(x*sigma*np.sqrt(2*np.pi))*np.exp(-(np.log(x)-mu)**2/(2*sigma**2))cdf=(1+scipy.special.erf((np.log(x)-mu)/(np.sqrt(2)*sigma)))/2p2=make_plot("Log Normal Distribution (μ=0, σ=0.5)",hist,edges,x,pdf,c...
Seaborn是一个基于Python的数据可视化库,它建立在Matplotlib库之上,提供了更高级的接口用于绘制统计图形。Seaborn的目标是使复杂的数据可视化工作变得更加简单和直观,同时生成具有吸引力、信息丰富的图形。它特别适合于探索性和解释性数据分析任务。下面是一些Seaborn的关键特性和功能: ...
Reversed and Complementary CDF plots¶ By default, the Y value represents the fraction of the data that is at or below the value on on the X axis. Setting ecdfmode to "reversed" reverses this, with the Y axis representing the fraction of the data at or above the X value. Setting ecd...
[2]A3 Let the joint cdf of X and Y be given byFX,Y (x, y) =0 if x 0 or y 0min{x, y} if x, y 0 and (x 1 or y 1)1 if x, y 1.(a) Compute P(0 X 1, 0 Y 1). [2](b) Compute the marginal cdf FY of Y . [3](c) Compute P(X 1/2 | Y 1/2).[...
t.cdf(abs(self.t), self.df_e)) * 2 # coef. p-values self.R2 = 1 - self.e.var()/self.y.var() # model R-squared self.R2adj = 1-(1-self.R2)*((self.nobs-1)/(self.nobs-self.ncoef)) # adjusted R-square self.F = (self.R2/self.df_r) / ((1-self.R2)/self.df_...
This leads to a recalibrated probability distribution that, however, has no analytical representation but is given by certain points defining a CDF distribution. Non-parametric calibration methods are netcal.regression.IsotonicRegression and netcal.regression.GPBeta. In contrast, parametric calibration ...
Projects Security Insights Additional navigation options master Branches 17Tags Code This branch is1137 commits behindawslabs/gluonts:dev. Folders and files Name Last commit message Last commit date Latest commit mbohlkeschneider Marginal CDFtoGaussianTransformation (awslabs#486) ...